In many late model consumer electronics devices such as desktop computers, laptop computers, smartphones, and tablet computers, there are multiple sound pick up channels in the form of two or more microphones. These produce mixed audio signals, which contain sounds from various or diverse sources in the acoustic environment, e.g., two or more talkers in the room along with a speakerphone and some ambient background noise (e.g., air conditioning), during a group conference call. Also, when a talker in a room is sufficiently far away from the microphones (e.g., in the far-field), the room reverberation muddies the speech signal produced by the microphones, resulting in increased word error rates by an automatic speech recognizer (ASR.) Dereverberation techniques have been proposed that use multi-channel linear prediction (MCLP) to predict the undesired reverberant components in the microphone signals, which are then removed before passing the microphone signals on to further processing (and ultimately, to the ASR.) An example is MCLP using the recursive least squares (RLS) algorithm. But prior art solutions are too complex to be easily “tuned” for a given application, and can be numerically unstable, especially for some online (real-time) ASR applications such as a voice triggered intelligent personal assistant (virtual assistant.) The virtual assistant needs to both accurately and rapidly detect an initial voice trigger phrase so that it can respond with reduced latency. To achieve natural human-machine interaction, the virtual assistant should be able to produce and display each recognized word immediately after it has been spoken, but it also has to remain numerically stable to avoid frustrating the user, while being computationally light so as to be implementable in a device such as a smartphone that has limited computing resources.